Dataset: ZuCo \(2.0\)  dataset [1]} provides simultaneous EEG and eye-tracking data for 18 participants reading a total of 739 sentences (349 for NR and 390 sentences for task-specific reading). The data of 12 participants (\(66.67\%\) ) is used for train, 2 for validation (\(16.67\) %) and 4 for testing (33%). Training Hyperparameters:  We summarize the range of hyperparameters as follows: dropout \(\delta \in [0, 0.8]\) , learning rate \(\eta \in \lbrace  10^{−3}, 10^{−2}, 10^{−1} \rbrace \) , batch size \(b \in \lbrace 4,8,16,32,64\rbrace \)  and epochs \((< 50)\) . Adam was employed for optimizing the cross entropy loss of the model. The proposed model was trained on Pytorch using NVIDIA RTX 2080Ti, with batch size 4 with the learning rate \(0.01\)  to converge in 20 epochs. The input word-level fixation segmented EEG signal was padded to 168, the max length of the input across the dataset. The hyperparameters of the Adaptive Graph Learning layer - saturation coefficient (\(\omega \) ), matrix regularization constant (\(\lambda \) ), and the temperature of Softmax smoothing (\(\tau \) ) are all set to \(0.5\)  for best performance. The top-k edge connections in the same layer are experimented with the value of \(k \in [1,20]\) . Empirically, the depth of the node feature aggregation \(K=2\)  and the depth regularization coefficients (\(\beta _1\) , \(\beta _2\) ) are set to \(0.5\)  and \(0.6\) , respectively in the DN-GCN layer. The filter sizes \(f_{1xD}\)  are experimentally found to be most effective when \(D\le 7\) . Layer Normalization is applied after each graph convolution module.
